Wireless propagation models play a significant role in the deployments of base stations that are used to the reference signal receiving power (RSRP) of signal receivers in a cell. However, the existing models predict the RSRP of one receiver point in a cell at a time, which cannot be generalized to other cells. Motivated by this, a cell-level RSRP estimation method is proposed to directly predict the whole-cell RSRP by converting the RSRP estimation into an image-to-image translation. First, an environment map of each cell and measured RSRP for each cell is transformed into an image. Second, a cell-level image-to-image wireless propagation model based on conditional generative adversarial networks is proposed, which can directly predict the whole-cell RSRP at a time. In particular, a residual estimation method is proposed for the measurement RSRP data in the real world. The proposed method employs an empirical model to reveal the wireless propagation law as a priori knowledge and guide the training steps of the deep learning model. Finally, the experimental results verify the accuracy and generalization performance of the proposed image-to-image wireless propagation model.

In this letter, an environmental features (EFs) extraction model is proposed for estimating reference signal received power (RSRP) accurately. Firstly, 18-D measured data is transformed into 15-D physical features (PFs). Then 15-D PFs is reduced to 14-D by performing correlation analysis. Secondly, EFs are extracted from the environmental maps (EMs) by applying Convolution Neural Networks (CNNs). Finally, several Machine Learning Regressors (MLRs) are trained to predict RSRP combining PFs and EFs as inputs. The results, in test dataset, show that prediction performance of MLRs is improved through 14-D PFs, and is further improved in nonlinear MLRs combining PFs and EFs.

An ultrathin layer of deep-red phosphorescent emitter tris(1-phenylisoquinoline) iridium (III) (Ir(piq)₃) is inserted within different positions of the electron blocking layer fac-tris (1-phenylpyrazolato-N,C^2′)-iridium(III) (Ir(ppz)₃) to distinguish the contribution of the emission from the triplet exciton energy transfer/diffusion from the adjacent blue phosphorescent emitter and the trap-assisted recombination from the narrow band-gap emitter itself. The charge trapping effect of the narrow band-gap deep-red emitter which forms a quantum-well-like structure also plays a role in shaping the electroluminescent characteristics of multi-color organic light-emitting diodes. By accurately controlling the position of the ultrathin sensing layer, it is considerably easy to balance the white emission which is quite challenging for full-color devices with multiple emission zones. There is nearly no energy transfer detectable if 7 nm thick Ir(ppz)₃ is inserted between the blue phosphorescent emitter and the ultrathin red emitter.

This paper proposes a novel semantic communication approach named Mamba Vision-based Semantic Communication (MVSC) for image transmission with integrated Signal-to-Noise Ratio (SNR) estimation. Unlike prior works that assume the SNR of the received signal is known and input a predetermined SNR value into a deep learning (DL) network, MVSC introduces an implicit SNR estimation module, allowing the network to infer channel conditions for SNR adaptation. To further improve performance, we propose the MVSC4, a joint-optimized of MVSC, which is trained using a multi-task learning strategy that simultaneously optimizes image reconstruction, SNR estimation, signal denoising, and image classification. This joint optimization enhances the network’s robustness to varying SNR conditions, particularly in low-SNR environments. Comparative experiments on CIFAR-10 and Kodak datasets demonstrate that MVSC4 outperforms both CNN-based and Transformer-based methods in terms of Peak Signal-to-Noise Ratio (PSNR) and Multiscale Structural Similarity (MS-SSIM). The results demonstrate the effectiveness and robustness of the proposed approach.

Information about college students’ social networks plays a pivotal role in college students’ mental health monitoring and student management. While there have been many studies to infer social networks by data mining, the mining of college students’ social networks lacks consideration of homophily. College students’ social behaviors show significant homophily in the aspect of major and grade. Consequently, the inferred inter-major and inter-grade social ties will be erroneously omitted without considering such an effect. In this work, we aimed to increase the fidelity of the extracted networks by alleviating the homophily effect. To achieve this goal, we propose a method that combines the sliding time-window method with the hierarchical encounter model based on association rules. Specifically, we first calculated the counts of spatial–temporal co-occurrences of each student pair. The co-occurrences were acquired by the sliding time-window method, which takes advantage of the symmetry of the social ties. We then applied the hierarchical encounter model based on association rules to extract social networks by layer. Furthermore, we propose an adaptive method to set co-occurrence thresholds. Results suggested that our model infers the social networks of students with better fidelity, with the proportion of extracted inter-major social ties in entire social ties increasing from 0.89% to 5.45% and the proportion of inter-grade social ties rising from 0.92% to 4.65%.

In vehicular ad hoc networks (VANETs), vehicles and roadside units (RSUs) utilize open wireless channels to exchange safety-critical data, facilitating real-time decision-making for enhanced road safety and traffic management efficiency in intelligent transportation systems (ITSs). However, the openness of these channels exposes them to various security threats. Malicious adversaries may impersonate RSUs to forge and distribute harmful commands, manipulating vehicular behavior, or masquerade as legitimate vehicles to bypass authentication protocols and gain unauthorized access. Such attacks jeopardize the security and functionality of the VANETs, underscoring the necessity of robust mutual authentication between vehicles and RSUs. Existing centralized trust authority (TA)-dependent schemes for vehicle-to-RSU (V2R) authentication incur high-computational overhead, introduce authentication latency, and cause a single point of failure, particularly in dense traffic scenarios. To address these challenges, we propose efficient, revocable authentication scheme with session key negotiation (ERAS2KN), an efficient and revocable authentication scheme with session key negotiation. By integrating physical unclonable functions (PUFs) with lightweight cryptography, such as one-way hash functions, bitwise xor, and symmetric encryption, ERAS2KN enables rapid mutual authentication and secure session key establishment. Comprehensive security analysis, including informal evaluation, formal security proof based on the real-or-random (RoR) model, and automated validation using AVISPA, confirms ERAS2KN’s resilience against vehicle impersonation, eavesdropping, vehicle/RSU compromise, man-in-the-middle, and other advance attacks. Performance evaluations demonstrate that ERAS2KN surpasses existing schemes by delivering enhanced security features while achieving the lowest computational overhead, communication overhead, and energy consumption cost, making it ideal for high-density VANETs environments.